Depth estimation.
We evaluate the performance of MIM and supervised pre-trained models on the NYUv2 [1]} and KITTI [2]} monocular depth estimation datasets.
The NYUv2 dataset includes 464 indoor scenes captured by a Microsoft Kinect camera. The official training split (\(24K\)  images) is used for training and we report the RMSE (Root Mean Square Error) on the 654 testing images from 215 indoor scenes.
The KITTI dataset contains various driving scenes. The Eigen split [3]} contains \(23K\)  training images and 697 testing images.
To compare with the previous approaches [4]}, [5]}, we set the maximum range as 10m for NYUv2 and 80m for KITTI.
